Mechano-Graded Contact-Electrification Interfaces Based Artificial Mechanoreceptors for Robotic Adaptive Reception

接触带电 材料科学 摩擦电效应 卷积神经网络 机器人 灵敏度(控制系统) 线性 变形(气象学) 弯曲 计算机科学 电子皮肤 人工智能 纳米技术 复合材料 电子工程 工程类
作者
Lei Hao,Yixin Cao,Gang Sun,Peihao Huang,Xu Xue,Bohan Lu,Jiawei Yan,Yuxi Wang,Eng Gee Lim,Xin Tu,Yina Liu,Xuhui Sun,Chun Zhao,Zhen Wen
出处
期刊:ACS Nano [American Chemical Society]
标识
DOI:10.1021/acsnano.4c14285
摘要

Triboelectrification-based artificial mechanoreceptors (TBAMs) is able to convert mechanical stimuli directly into electrical signals, realizing self-adaptive protection and human–machine interactions of robots. However, traditional contact–electrification interfaces are prone to reaching their deformation limits under large pressures, resulting in a relatively narrow linear range. In this work, we fabricated mechano-graded microstructures to modulate the strain behavior of contact–electrification interfaces, simultaneously endowing the TBAMs with a high sensitivity and a wide linear detection range. The presence of step regions within the mechanically graded microstructures helps contact–electrification interfaces resist fast compressive deformation and provides a large effective area. The highly sensitive linear region of TBAM with 1.18 V/kPa can be effectively extended to four times of that for the devices with traditional interfaces. In addition, the device is able to maintain a high sensitivity of 0.44 V/kPa even under a large pressure from 40 to 600 kPa. TBAM has been successfully used as an electronic skin to realize self-adaptive protection and grip strength perception for a commercial robot arm. Finally, a high angle resolution of 2° and an excellent linearity of 99.78% for joint bending detection were also achieved. With the aid of a convolutional neural network algorithm, a data glove based on TBAMs realizes a high accuracy rate of 95.5% for gesture recognition in a dark environment.
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